7 papers
TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning
Varun Belagali, Saarthak Kapse, Pierre Marza +12
The interpretation of small tiles in large whole slide images (WSI) often needs a larger image context. We introduce TICON, a transformer-based tile representation contextualizer t…
PixCell: A generative foundation model for digital histopathology images
Srikar Yellapragada, Alexandros Graikos, Zilinghan Li +11
The digitization of histology slides has revolutionized pathology, providing massive datasets for cancer diagnosis and research. Self-supervised and vision-language models have bee…
CDG-MAE: Cross-view Masked Modeling using Diffusion Generated Views
Varun Belagali, Pierre Marza, Srikar Yellapragada +7
Cross-view masked autoencoding has emerged as a powerful pretext task for learning dense correspondences, which are essential for applications such as video label propagation. The…
Enhancing Clinical Models with Pseudo Data for De-identification
Paul Landes, Aaron J Chaise, Tarak Nath Nandi +1
Many models are pretrained on redacted text for privacy reasons. Clinical foundation models are often trained on de-identified text, which uses special syntax (masked) text in plac…
Pathology Image Compression with Pre-trained Autoencoders
Srikar Yellapragada, Alexandros Graikos, Kostas Triaridis +6
The growing volume of high-resolution Whole Slide Images in digital histopathology poses significant storage, transmission, and computational efficiency challenges. Standard compre…
Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning
Varun Belagali, Srikar Yellapragada, Alexandros Graikos +7
Self-supervised learning (SSL) methods have emerged as strong visual representation learners by training an image encoder to maximize similarity between features of different views…